AI-driven Denial Management
by Xsolis
· Based in United States →
— AI-driven denial management empowers hospitals to reduce claim denials, cut revenue leakage, and streamline operations through proactive, intelligent automation.
Overview
Xsolis is an AI-driven technology company focused on improving healthcare operations through cognitive computing. They leverage AI, machine learning, and data science to facilitate the sharing of data and actionable insights, reducing friction between providers and payers. Their Dragonfly platform, previously known as CORTEX, is an AI-driven proprietary platform that uses real-time predictive analytics to continuously assign an objective medical necessity score and assess the anticipated level of care for every patient. This technology aims to break down healthcare silos and enable people over process, creating a more efficient and frictionless healthcare system. Xsolis offers solutions for revenue integrity, utilization management, and physician advisory services.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- AI-powered denial management services
- Root-cause analytics for denials
- Automated workflows for denial resolution
- Identification and prioritization of denial resolution cases with clinical merit
- Defensible admissions decisions to payers (concurrently and retrospectively)
- Evidence-based analysis for appeals
- Actionable denial management analytics for key case metrics
- Real-time predictive analytics for medical necessity scoring (Care Level Scoreu2122)
- End-to-end denial data in one centralized place
- Proactive identification of denial-prone patterns
Use Cases
- Reducing claim denials and revenue leakage
- Streamlining denial and appeal processes
- Optimizing concurrent authorization processes
- Enhancing revenue integrity with AI-driven analytics
- Supporting physician advisor activities with denial trends
- Improving payer-provider collaboration and relationships
What Physicians Need to Know
Key Capabilities
Xsolis' AI-driven Denial Management solution utilizes machine learning algorithms to identify and prioritize denial resolution cases with strong clinical merit. It offers root-cause analytics, automated workflows, and actionable denial management analytics for key case metrics like diagnosis, procedure, length-of-stay, and denial outcomes. The system also supports concurrent and retrospective defense of admissions decisions to payers. It can automate claims processing to reduce manual errors and proactively manage denial risks.
Clinical Utility
The tool helps healthcare professionals by identifying denial resolution cases with demonstrated clinical merit through an analytics-based approach. It aims to reduce administrative burden on case managers and utilization management staff, allowing them to focus on complex cases. The platform, Dragonfly, provides real-time, AI-driven workflow technology for medical necessity reviews, offering immediate alerts on patient changes and facilitating escalation for physician advisor review.
Integration Options
Dragonfly interfaces directly with Electronic Medical Record (EMR) systems, such as Epic, to offer real-time, AI-driven workflow technology.
Compliance Status
Xsolis' team decisions are supported by compliant, AI-driven insights backed by historical data. The AI-driven software reviews clinical notes to ensure alignment with coding and billing rules, including CMS guidelines, DRG assignment, and ICD-10 coding structures, flagging insufficient documentation or mismatches that could trigger audits or denials.
User Experience
The platform is designed to streamline mid-revenue cycle workflows and unify real-time clinical data, reducing variability and manual effort. Users have reported high satisfaction, with 91% satisfied or highly satisfied with overall performance, and 89% relying on its AI to minimize preventable denials.
Support Quality
Xsolis offers professional denial management and recovery services, with a team of clinical and legal experts undertaking denial and appeal management. They also provide expert-led trainings and resources for clients.
Implementation Complexity
The solution aims for rapid, tangible results, with nearly 9 in 10 users seeing outcomes within the first year of implementation.
Evidence Base
Xsolis leverages AI and machine learning, with over 9.5 billion predictions and growing. A KLAS Second Look Report in 2025 indicated that Dragonfly delivers measurable outcomes, with users reporting improved denial rates, reduced length of stay, and rapidly achieved ROI. Citizens Medical Center, a partner for over 7 years, reported a 12% drop in denials and payer response times cut in half.
Physician Tip
Leverage the AI-driven insights to prioritize your review efforts on complex cases with higher clinical merit, rather than spending time on obvious status determinations. Utilize the real-time alerts for documentation risks to make proactive corrections during a patient's stay, preventing denials before they occur. Engage with the platform's analytics to understand denial trends and root causes, informing your documentation practices and peer-to-peer conversations with payers. The automated clinical summaries for appeal letters can significantly reduce administrative burden, allowing more focus on patient care.
The Xsolis Dragonfly platform is designed to integrate directly with Electronic Medical Record (EMR) systems, such as Epic, to provide real-time, AI-driven workflows and data exchange. This integration is crucial for seamless operation and maximizing the benefits of the AI-driven denial management and utilization review processes.
Details
| Category | Medical Billing & RCM |
| Pricing | Unknown — unknown |
| Deployment | Cloud-based (SaaS) |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status | Unknown AI-estimated — unknown |
| Integrations | |
| EHR | Not specified |
| Specialties | Family Medicine, Hospital Medicine, Internal Medicine |
What the Web Says
Xsolis' AI-driven Denial Management, particularly its Dragonfly platform, is generally viewed as a positive and transformative solution in healthcare. It aims to reduce administrative waste, improve medical necessity decision-making, and foster collaboration between healthcare providers and payers. The system uses predictive and generative AI to streamline utilization review, case management, and revenue cycle tasks, leading to faster approvals and reduced denials.
Overall: PositiveStrengths
- Improved denial rates and reduced length of stay (LOS).
- Rapid return on investment, with many users seeing outcomes within the first year.
- Enhanced payer-provider collaboration through shared AI platforms and objective data.
- Streamlined medical necessity reviews, with reported time savings (e.g., 68% faster initial reviews).
- Seamless integration with Electronic Health Records (EHRs) and responsive customer service.
- Reduces administrative burden for nurses and staff, allowing them to focus on complex cases and patient care.
Limitations
- Some users reported dissatisfaction with Xsolis' denial management services, citing slow turnaround times and inconsistent staff knowledge.
- Concerns about potential job displacement for nurses and social workers due to AI implementation, though some anxieties dissipated as staff found the AI platform allowed them to leverage their skills more effectively.
- One Reddit user mentioned potential double documentation for Utilization Review when integrating with Epic.
- A data breach affecting over 1.3 million individuals was reported in June 2026, highlighting AI-vendor concentration risk and data retention issues.
- A Stanford analysis highlighted general concerns about AI in utilization management, including automation bias, anchoring effects, opacity, and expertise gaps.
Based on reviews from: Becker's Hospital Review, Xsolis Website, FeaturedCustomers, TechTarget, KLAS Research, MD Clarity, PR Newswire, Reddit, AI-Tech Park, YouTube
Last updated: 2026-08-24
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Press & Coverage
Tebra
Automated Denial Management: How To Prevent and Resolve Denials Faster
AI-driven denial management tools categorize incoming denials, run root cause analysis, and route them to the right workflow automatically, significantly reducing manual review and improving efficiency. Forty-six percent of healthcare organizations already use AI for revenue cycle management, with another 49% planning to adopt it within 12 months.
2026-07
P3Care
How AI-Driven Denial Management Solutions for Faster Approvals
AI-driven denial management is transforming healthcare revenue cycles by reducing denials, speeding approvals, and improving cash flow through predictive analytics, automated claim resolution, and streamlined workflows. The future of AI in denial management is expected to be even smarter, faster, and more proactive.
2025-11
AnnexMed
AI in Denial Management Billing: 2026 Guide
AI is transforming denial management in medical billing by leveraging machine learning to analyze claims data, identify patterns, and predict potential denials before they occur. AI-driven denial management will seamlessly integrate with EHRs, ensuring clinical documentation aligns with billing requirements and drastically lowering denial rates.
2025-03
AAPC
AI-Driven Denial Management: Reduce Risk, Improve Cash Flow
AI-driven denial management helps healthcare teams work smarter, reduce denials, and get reimbursed faster by preventing denials, automating appeals, and monitoring trends. 83% of healthcare organizations experienced at least a 10% decrease in denials within six months of adopting AI.
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Kizen
The Denials Problem is Worse Than Ever in Healthcare: How AI Can Reduce Rework and Protect Revenue in 2026
AI is moving beyond isolated tools into orchestrated, end-to-end workflow automation for denial management, helping teams execute tasks faster and more consistently. AI can meaningfully reduce the impact of denials by removing the rework loop that makes them so expensive.
2025-12
Plutus Health
AI Denial Management: Revolutionizing Healthcare RCM
AI-driven denial management solutions enhance the speed of claim processing, improve accuracy, and ensure compliance with regulatory standards, leading to a more resilient revenue cycle. Studies suggest denials can be reduced by up to 30% with AI-powered denial management, improving the financial condition of healthcare organizations.
2024-01
ResearchGate
Transforming the Healthcare Revenue Cycle with Artificial Intelligence in the USA
This study examines how AI-driven solutions are changing the U.S. healthcare revenue cycle by automating repetitive operations, identifying irregularities in claims, and predicting reimbursement patterns. AI-driven denial management tools guarantee adherence to changing payer regulations and regulatory mandates, improving U.S. RCM.
2026-08
Opus EHR
How AI Cuts Denials for Behavioral Health Providers
AI-driven denial management can lead to tangible improvements for behavioral health organizations, with research showing its effectiveness in reducing denials. AI-powered systems have boosted appeal success rates from 60% to 88%, with medical necessity appeals achieving success rates over 95%.
2026-03
Videos
Product demos, reviews, and walkthroughs for AI-driven Denial Management.
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Frequently Asked Questions
AI-driven denial management utilizes machine learning algorithms to analyze historical claims data, identify patterns in denials, and predict future denials before they occur. It can also automate the appeals process by generating appeal letters and identifying the specific reasons for denial, thereby streamlining the revenue cycle and reducing manual effort.
AI-driven denial management systems must be designed and implemented with robust security measures to ensure HIPAA compliance, including data encryption, access controls, and audit trails. Vendors should provide clear documentation of their compliance protocols and how their AI models handle protected health information (PHI) to maintain patient privacy and data security.
Yes, alternatives include traditional manual denial management processes, outsourcing to third-party medical billing companies specializing in denial appeals, or utilizing more basic RCM software with built-in denial tracking features. While these may require more human intervention, they can be viable for practices with lower claim volumes or specific budget constraints.
Pricing models vary but often include a subscription fee based on claim volume, a percentage of recovered revenue, or a hybrid approach. The ROI can be significant due to reduced denial rates, faster claim resolution, decreased administrative costs, and improved cash flow, though specific returns depend on the practice's current denial rate and the solution's effectiveness.
Limitations can include the initial cost of implementation, the need for clean and comprehensive historical data to train the AI effectively, and the potential for 'black box' issues where the AI's decision-making process isn't entirely transparent. Over-reliance on AI without human oversight could also lead to missed nuances in complex denial cases.
While some improvements may be seen relatively quickly, it typically takes a few months to fully integrate the system, train the AI with your specific data, and optimize workflows. Significant and consistent improvements in denial rates and revenue recovery are often observed within 3 to 6 months of full implementation.
Most reputable AI-driven denial management solutions are designed to integrate seamlessly with popular EHR and practice management systems through APIs or other interoperability standards. It's crucial to verify compatibility with your specific software during the vendor selection process to ensure smooth data flow and avoid disruptions.
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| Last Verified: 2026-08-23 | First Added: 2026-08-23